15-09-2021
German Credit Data Set Arff Download
This is an analysis and classification of german credit data (more information at this pdf). Three classifiers tested, Support Vector Machines (SVM), Random Forests, Naive Bayes, to select the most efficient for our data. Few datasets: Credit Card Fraud Detection at Kaggle The datasets contains transactions made by credit cards in September 2013 by european cardholders. This dataset present transactions that occurred in two days, where we have 492 frauds out of 2.
Attribute Details:
German Credit Data Set Arff Download Pdf
| Name | Type | Description |
|---|---|---|
| checking_account_status | string | Status of existing checking account (A11: < 0 DM, A12: 0 <= x < 200 DM, A13 : >= 200 DM / salary assignments for at least 1 year, A14 : no checking account) |
| duration | integer | Duration in month |
| credit_history | string | A30: no credits taken/ all credits paid back duly, A31: all credits at this bank paid back duly, A32: existing credits paid back duly till now, A33: delay in paying off in the past, A34 : critical account/ other credits existing (not at this bank) |
| purpose | string | Purpose of Credit (A40 : car (new), A41 : car (used), A42 : furniture/equipment, A43 : radio/television, A44 : domestic appliances, A45 : repairs, A46 : education, A47 : (vacation - does not exist?), A48 : retraining, A49 : business, A410 : others) |
| credit_amount | float | |
| savings | string | Savings in accounts/bonds (A61 : < 100 DM, A62 : 100 <= x < 500 DM, A63 : 500 <= x < 1000 DM, A64 : >= 1000 DM, A65 : unknown/ no savings account |
| present_employment | string | A71 : unemployed, A72 : < 1 year, A73 : 1 <= x < 4 years, A74 : 4 <= x < 7 years, A75 : .. >= 7 years |
| installment_rate | float | Installment Rate in percentage of disposable income |
| personal | string | Personal Marital Status and Sex (A91 : male : divorced/separated, A92 : female : divorced/separated/married, A93 : male : single, A94 : male : married/widowed, A95 : female : single) |
| other_debtors | string | A101 : none, A102 : co-applicant, A103 : guarantor |
| present_residence | float | Present residence since |
| property | string | A121 : real estate, A122 : if not A121 : building society savings agreement/ life insurance, A123 : if not A121/A122 : car or other, not in attribute 6, A124 : unknown / no property |
| age | float | Age in years |
| other_installment_plans | string | A141 : bank, A142 : stores, A143 : none |
| customer_type | integer | Predictor Class: 1=Good, 2=Bad |
German Credit Data Set Arff Downloads
Showing 15 out of 21 attributes. Download attribute CSV for full details